Abstract

We have applied a Bayesian neural network (BNN) to the task of distinguishing between true-positive (TP) and false-positive (FP) detected clusters in a computer-aided diagnosis (CAD) scheme for detecting clustered microcalcifications in mammograms. Because BNNs can approximate ideal observer decision functions given sufficient training data, this approach should have better performance than our previous FP cluster elimination methods. Eight cluster-based features were extracted from the TP and FP clusters detected by the scheme in a training dataset of 39 mammograms. This set of features was used to train a BNN with eight input nodes, five hidden nodes, and one output node. The trained BNN was tested on the TP and FP clusters detected by our scheme in an independent testing set of 50 mammograms. The BNN output was analyzed using ROC and FROC analysis. The detection scheme with the BNN for FP cluster elimination had substantially better cluster sensitivity at low FP rates (below 0.8 FP clusters per image) than the original detection scheme without the BNN. Our preliminary research shows that a BNN can improve the performance of our scheme for detecting clusters of microcalcifications.

title = "Eliminating false-positive microcalcification clusters in a mammography CAD scheme using a Bayesian neural network",

abstract = "We have applied a Bayesian neural network (BNN) to the task of distinguishing between true-positive (TP) and false-positive (FP) detected clusters in a computer-aided diagnosis (CAD) scheme for detecting clustered microcalcifications in mammograms. Because BNNs can approximate ideal observer decision functions given sufficient training data, this approach should have better performance than our previous FP cluster elimination methods. Eight cluster-based features were extracted from the TP and FP clusters detected by the scheme in a training dataset of 39 mammograms. This set of features was used to train a BNN with eight input nodes, five hidden nodes, and one output node. The trained BNN was tested on the TP and FP clusters detected by our scheme in an independent testing set of 50 mammograms. The BNN output was analyzed using ROC and FROC analysis. The detection scheme with the BNN for FP cluster elimination had substantially better cluster sensitivity at low FP rates (below 0.8 FP clusters per image) than the original detection scheme without the BNN. Our preliminary research shows that a BNN can improve the performance of our scheme for detecting clusters of microcalcifications.",

N2 - We have applied a Bayesian neural network (BNN) to the task of distinguishing between true-positive (TP) and false-positive (FP) detected clusters in a computer-aided diagnosis (CAD) scheme for detecting clustered microcalcifications in mammograms. Because BNNs can approximate ideal observer decision functions given sufficient training data, this approach should have better performance than our previous FP cluster elimination methods. Eight cluster-based features were extracted from the TP and FP clusters detected by the scheme in a training dataset of 39 mammograms. This set of features was used to train a BNN with eight input nodes, five hidden nodes, and one output node. The trained BNN was tested on the TP and FP clusters detected by our scheme in an independent testing set of 50 mammograms. The BNN output was analyzed using ROC and FROC analysis. The detection scheme with the BNN for FP cluster elimination had substantially better cluster sensitivity at low FP rates (below 0.8 FP clusters per image) than the original detection scheme without the BNN. Our preliminary research shows that a BNN can improve the performance of our scheme for detecting clusters of microcalcifications.

AB - We have applied a Bayesian neural network (BNN) to the task of distinguishing between true-positive (TP) and false-positive (FP) detected clusters in a computer-aided diagnosis (CAD) scheme for detecting clustered microcalcifications in mammograms. Because BNNs can approximate ideal observer decision functions given sufficient training data, this approach should have better performance than our previous FP cluster elimination methods. Eight cluster-based features were extracted from the TP and FP clusters detected by the scheme in a training dataset of 39 mammograms. This set of features was used to train a BNN with eight input nodes, five hidden nodes, and one output node. The trained BNN was tested on the TP and FP clusters detected by our scheme in an independent testing set of 50 mammograms. The BNN output was analyzed using ROC and FROC analysis. The detection scheme with the BNN for FP cluster elimination had substantially better cluster sensitivity at low FP rates (below 0.8 FP clusters per image) than the original detection scheme without the BNN. Our preliminary research shows that a BNN can improve the performance of our scheme for detecting clusters of microcalcifications.